Forecasting with LSTM

Load library

pacman::p_load(dplyr, ggplot2, lubridate, ggthemes, remotes,
               neuralnet, Metrics, tidyverse, readxl, knitr, 
               glue, forcats, timetk, tidyquant, tibbletime,
               recipes, rsample, yardstick)

#install_github("rstudio/tensorflow")
library(tensorflow)
#install_tensorflow(method = "conda", envname = "r-reticulate")
library(keras)

Attaching package: ‘keras’

The following object is masked from ‘package:yardstick’:

    get_weights

Load data

mydata <- read_csv("UNVR.JK_long.csv", 
                   col_types = cols(Date = col_date(format = "%m/%d/%Y")))
mydata <- mydata[1443:4642,]

Visualisasi data time series

mydata %>% 
  ggplot(aes(x = Date, y = Close)) +
  geom_line(color = "mediumpurple2") + 
  geom_point(size = 0.2, color = "slateblue3") +
  theme_hc() +
  labs(
    title = 'UNVR Price',
    subtitle = 'Period Jun-2009 to Apr-2022') +
  xlab("Date") + ylab("Close Price")

Fokus pada kolom ‘Close’

Untuk analisis selanjutnya, selain kolom ‘Close’ kolom lain diabaikan. Kemudian data dilakukan diferensiasi untuk lebih mendapatkan kestatsioneritasan.

data <- mydata[,c(1,5)] %>% 
  pull(Close)

diffed <- diff(data, differences = 1)
head(diffed)

[1]   0  10 -10   0   0  80

Setelah didiferensiasi, dibentuk vector dengan lag hasil diferensiasi. Tujuannya untuk menganalogikan adanya variabel input (X) dan variabel output (Y), karena pada LSTM mensyaratkan hal tersebut. Dalam hal ini (x-t) adalah input sedangkan (x) sebagai output.

lag_transform <- function(x, k = 1){
  
  lagged =  c(rep(NA, k), x[1:(length(x)-k)])
  DF = as.data.frame(cbind(lagged, x))
  colnames(DF) <- c( paste0('x-', k), 'x')
  DF[is.na(DF)] <- 0
  return(DF)
}

supervised <- lag_transform(diffed, 1)
head(supervised)

NA

Splitting data; Training dan Testing

Alokasi 80:20

N <- nrow(supervised)
n <- round(N * 0.8, digits = 0)
train <- supervised[1:n, ]
test  <- supervised[(n+1):N,  ]

Normalisasi data

scale_data <- function(train, test, feature_range = c(0, 1)) {
  x = train
  fr_min = feature_range[1]
  fr_max = feature_range[2]
  std_train = ((x - min(x) ) / (max(x) - min(x)  ))
  std_test  = ((test - min(x) ) / (max(x) - min(x)  ))
  
  scaled_train = std_train *(fr_max -fr_min) + fr_min
  scaled_test = std_test *(fr_max -fr_min) + fr_min
  
  return( list(scaled_train = as.vector(scaled_train), scaled_test = as.vector(scaled_test) ,scaler= c(min =min(x), max = max(x))) )
  
}

Scaled <- scale_data(train, test, c(-1, 1))

y_train <- Scaled$scaled_train[, 2]
x_train <- Scaled$scaled_train[, 1]

y_test <- Scaled$scaled_test[, 2]
x_test <- Scaled$scaled_test[, 1]

Inverse transform

Fungsi ini digunakan untuk mengembalikan nilai yang sebelumnya dinormalisasi

invert_scaling <- function(scaled, scaler, feature_range = c(0, 1)){
  min = scaler[1]
  max = scaler[2]
  t = length(scaled)
  mins = feature_range[1]
  maxs = feature_range[2]
  inverted_dfs = numeric(t)
  
  for( i in 1:t){
    X = (scaled[i]- mins)/(maxs - mins)
    rawValues = X *(max - min) + min
    inverted_dfs[i] <- rawValues
  }
  return(inverted_dfs)
}

Modelling

Dalam LSTM diperlukan 3 dimensi array sebagain input shape.

dim(x_train) <- c(length(x_train), 1, 1)
X_shape2 <- dim(x_train)[2]
X_shape3 <- dim(x_train)[3]
batch_size <- 1            
units <- 1 

Pembentukan model LSTM

model <- keras_model_sequential() 

2022-05-07 07:44:06.462791: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2022-05-07 07:44:06.465408: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Loaded Tensorflow version 2.8.0
2022-05-07 07:44:20.151737: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2022-05-07 07:44:20.153390: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2022-05-07 07:44:20.155201: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-HRK1NGB
2022-05-07 07:44:20.156367: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-HRK1NGB

model %>% 
  layer_lstm(units, batch_input_shape = c(batch_size, X_shape2, X_shape3),
             stateful= TRUE) %>%
  layer_dense(units = 1)

Mengevaluasi model dengan parameter MSE

model %>% compile(
  loss = 'mean_squared_error',
  optimizer = optimizer_adam(learning_rate = 0.02, decay = 1e-6 ))

summary(model)

Model: "sequential"
_____________________________________________________________________________
 Layer (type)                     Output Shape                   Param #     
=============================================================================
 lstm (LSTM)                      (1, 1)                         12          
                                                                             
 dense (Dense)                    (1, 1)                         2           
                                                                             
=============================================================================
Total params: 14
Trainable params: 14
Non-trainable params: 0
_____________________________________________________________________________

Hasil (progres) Evaluasi Model

Epochs = 50   
for(i in 1:Epochs ){
  model %>% fit(x_train, y_train, epochs = 1, 
                 batch_size = batch_size, verbose = 1, shuffle = FALSE)
  model %>% reset_states()
}

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WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0041s vs `on_train_batch_end` time: 0.0043s). Check your callbacks.


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Membuat Prediksi

L <- length(x_test)
scaler <- Scaled$scaler
prediksi <- numeric(L)

for(i in 1:L){
  X = x_test[i]
  dim(X) = c(1,1,1)
  yhat = model %>% predict(X, batch_size = batch_size)
  # invert scaling
  yhat = invert_scaling(yhat, scaler,  c(-1, 1))
  # invert differencing
  yhat  = yhat + data[(n+i)]
  # store
  prediksi[i] <- yhat
}

Menggabungkan Data

Penggabungan data aktual, training set, testing set, dan prediksi

dataplot <- mydata[,c(1,5)]
train_plot <- dataplot[1:n, ]
test_plot  <- dataplot[(n+1):N,  ]

plotpred <- dataplot %>%
  rename(Actual = Close) %>%
  left_join(
    tibble(
      Date = train_plot$Date,
      Train = pull(train_plot, Close))) %>%
  left_join(
    tibble(
      Date = test_plot$Date,
      Test = pull(test_plot, Close))) %>%
  left_join(
    tibble(
      Date = test_plot$Date,
      Pred = prediksi))

Joining, by = "Date"
Joining, by = "Date"
Joining, by = "Date"

Membuat Plot

Plot yang menggambarkan data aktual (training + testing), training, testing, dan prediksi dari awal

options(warn = -4)
plotpred %>% 
  gather(
    key = key, value = value,
    Actual, Train, Test, Pred
  ) %>%
  mutate(
    key = key %>% factor(levels = c("Actual", "Train", "Test", "Pred"))
  ) %>%
  ggplot(aes(x = Date, y = value, colour = key)) +
  geom_line(size = 1) + 
  scale_color_manual(values = c(
    "Actual" = "mediumpurple2",
    "Train" = "mediumpurple3",
    "Test" = "tomato2",
    "Pred" = "olivedrab2"
  )) + theme_hc() + 
  labs(
    title = 'Close Price',
    subtitle = 'Comparison between Actual and Predicted') +
  xlab("Date") + ylab("Price")

Evaluasi Parameter Prediksi

evaldata <- plotpred[2560:3199,]
rmse(evaldata$Test, evaldata$Pred)

[1] 30.43851

mse(evaldata$Test, evaldata$Pred)

[1] 926.503

Untuk lebih memberikan gambaran jelas nilai antara testing dan prediksi dibentuk plot berdasarkan periode data testing

evaldata %>% 
  gather(
    key = key, value = value,
    Test, Pred
  ) %>%
  mutate(
    key = key %>% factor(levels = c("Test", "Pred"))
  ) %>%
  ggplot(aes(x = Date, y = value, colour = key)) +
  geom_line(size = 0.6) + 
  scale_color_manual(values = c(
    "Test" = "tomato2",
    "Pred" = "olivedrab2"
  )) + theme_hc() + 
  labs(
    title = 'Close Price Test Dataset',
    subtitle = 'Comparison between Actual and Predicted') +
  xlab("Date") + ylab("Price")

Dari plot tersebut dan evaluasi parameter prediksi nampaknya nilai yang diprediksi pada data testing sudah cukup baik.

Forecasting

Misal dilakukan prediksi pengamatan untuk 120 periode ke depan, dibuatkan fungsi prediksinya sebagai berikut

future_predict <- function(data, epochs = 300, ...) {
    
    lstm_prediction <- function(data, epochs, ...) {
        df <- data
        
        rec_obj <- recipe(value ~ ., df) %>%
            step_sqrt(value) %>%
            step_center(value) %>%
            step_scale(value) %>%
            prep()
        
        df_processed_tbl <- bake(rec_obj, df)
        
        center_history <- rec_obj$steps[[2]]$means["value"]
        scale_history  <- rec_obj$steps[[3]]$sds["value"]
        
        lag_setting  <- 120 #prediksi
        batch_size   <- 40
        train_length <- 440
        tsteps       <- 1
        epochs       <- epochs
        
        lag_train_tbl <- df_processed_tbl %>%
            mutate(value_lag = lag(value, n = lag_setting)) %>%
            filter(!is.na(value_lag)) %>%
            tail(train_length)
        
        x_train_vec <- lag_train_tbl$value_lag
        x_train_arr <- array(data = x_train_vec, dim = c(length(x_train_vec), 1, 1))
        
        y_train_vec <- lag_train_tbl$value
        y_train_arr <- array(data = y_train_vec, dim = c(length(y_train_vec), 1))
        
        x_test_vec <- y_train_vec %>% tail(lag_setting)
        x_test_arr <- array(data = x_test_vec, dim = c(length(x_test_vec), 1, 1))
                
        model <- keras_model_sequential()

        model %>%
            layer_lstm(units            = 50, 
                       input_shape      = c(tsteps, 1), 
                       batch_size       = batch_size,
                       return_sequences = TRUE, 
                       stateful         = TRUE) %>% 
            layer_lstm(units            = 50, 
                       return_sequences = FALSE, 
                       stateful         = TRUE) %>% 
            layer_dense(units = 1)
        
        model %>% 
            compile(loss = 'mean_squared_error', optimizer = 'adam')
        

        for (i in 1:epochs) {
            model %>% fit(x          = x_train_arr, 
                          y          = y_train_arr, 
                          batch_size = batch_size,
                          epochs     = 1, 
                          verbose    = 1, 
                          shuffle    = FALSE)
            
            model %>% reset_states()
            cat("Epoch: ", i)
            
        }
        
        # Membuat prediksi
        pred_out <- model %>% 
            predict(x_test_arr, batch_size = batch_size) %>%
            .[,1] 
        
        idx <- data %>%
            tk_index() %>%
            tk_make_future_timeseries(length_out = lag_setting)
        
        pred_tbl <- tibble(
            index   = idx,
            value   = (pred_out * scale_history + center_history)^2
            )
        
        tbl_1 <- df %>%
            add_column(key = "actual")

        tbl_3 <- pred_tbl %>%
            add_column(key = "predict")

        time_bind_rows <- function(data_1, data_2, index) {
            index_expr <- enquo(index)
            bind_rows(data_1, data_2) %>%
                as_tbl_time(index = !! index_expr)
        }

        ret <- list(tbl_1, tbl_3) %>%
            reduce(time_bind_rows, index = index) %>%
            arrange(key, index) %>%
            mutate(key = as_factor(key))

        return(ret)
        
    }
    
    safe_lstm <- possibly(lstm_prediction, otherwise = NA)
    
    safe_lstm(data, epochs, ...)
    
}

Hasil forecasting

#membuat dataframe baru
data_for_predict <- mydata[,c(1,5)]

colnames(data_for_predict)[1] <- "index"
colnames(data_for_predict)[2] <- "value"

#forecasting
future_value <- future_predict(data_for_predict, epochs = 300)

2022-05-08 09:15:50.214761: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2022-05-08 09:15:50.219322: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Loaded Tensorflow version 2.8.0
2022-05-08 09:16:33.911391: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2022-05-08 09:16:33.914367: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2022-05-08 09:16:33.922908: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-HRK1NGB
2022-05-08 09:16:33.924064: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-HRK1NGB

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Epoch:  1
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Epoch:  2
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Epoch:  3
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Epoch:  4
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Epoch:  5
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Epoch:  6
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Epoch:  7
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Epoch:  8
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11/11 [==============================] - 0s 11ms/step - loss: 0.2117
Epoch:  9
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11/11 [==============================] - 0s 11ms/step - loss: 0.2000
Epoch:  10
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11/11 [==============================] - 0s 10ms/step - loss: 0.1732
Epoch:  11
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Epoch:  12
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Epoch:  13
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Epoch:  14
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11/11 [==============================] - 0s 11ms/step - loss: 0.1241
Epoch:  15
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11/11 [==============================] - 0s 11ms/step - loss: 0.1090
Epoch:  16
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Epoch:  17
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Epoch:  18
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Epoch:  19
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11/11 [==============================] - 0s 10ms/step - loss: 0.0810
Epoch:  20
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11/11 [==============================] - 0s 12ms/step - loss: 0.0773
Epoch:  21
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Epoch:  22
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Epoch:  23
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Epoch:  24
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Epoch:  25
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Epoch:  26
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Epoch:  27
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Epoch:  28
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Epoch:  29
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Epoch:  30
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11/11 [==============================] - 0s 10ms/step - loss: 0.0393
Epoch:  31
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11/11 [==============================] - 0s 10ms/step - loss: 0.0369
Epoch:  32
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Epoch:  33
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11/11 [==============================] - 0s 9ms/step - loss: 0.0338
Epoch:  34
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11/11 [==============================] - 0s 10ms/step - loss: 0.0326
Epoch:  35
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Epoch:  36
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11/11 [==============================] - 0s 10ms/step - loss: 0.0308
Epoch:  37
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11/11 [==============================] - 0s 10ms/step - loss: 0.0300
Epoch:  38
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11/11 [==============================] - 0s 12ms/step - loss: 0.0293
Epoch:  39
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11/11 [==============================] - 0s 10ms/step - loss: 0.0286
Epoch:  40
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11/11 [==============================] - 0s 10ms/step - loss: 0.0280
Epoch:  41
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11/11 [==============================] - 0s 10ms/step - loss: 0.0274
Epoch:  42
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11/11 [==============================] - 0s 10ms/step - loss: 0.0269
Epoch:  43
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11/11 [==============================] - 0s 11ms/step - loss: 0.0264
Epoch:  44
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11/11 [==============================] - 0s 10ms/step - loss: 0.0260
Epoch:  45
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11/11 [==============================] - 0s 11ms/step - loss: 0.0256

11/11 [==============================] - 0s 13ms/step - loss: 0.0256
Epoch:  46
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11/11 [==============================] - 0s 10ms/step - loss: 0.0252
Epoch:  47
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11/11 [==============================] - 0s 11ms/step - loss: 0.0249

11/11 [==============================] - 0s 13ms/step - loss: 0.0249
Epoch:  48
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11/11 [==============================] - 0s 9ms/step - loss: 0.0246

11/11 [==============================] - 0s 10ms/step - loss: 0.0246
Epoch:  49
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11/11 [==============================] - 0s 9ms/step - loss: 0.0243

11/11 [==============================] - 0s 10ms/step - loss: 0.0243
Epoch:  50
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11/11 [==============================] - 0s 9ms/step - loss: 0.0240

11/11 [==============================] - 0s 10ms/step - loss: 0.0240
Epoch:  51
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11/11 [==============================] - 0s 13ms/step - loss: 0.0238
Epoch:  52
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11/11 [==============================] - 0s 10ms/step - loss: 0.0235
Epoch:  53
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11/11 [==============================] - 0s 10ms/step - loss: 0.0233
Epoch:  54
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11/11 [==============================] - 0s 10ms/step - loss: 0.0231
Epoch:  55
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11/11 [==============================] - 0s 14ms/step - loss: 0.0229
Epoch:  56
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11/11 [==============================] - 0s 9ms/step - loss: 0.0228
Epoch:  57
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11/11 [==============================] - 0s 9ms/step - loss: 0.0226

11/11 [==============================] - 0s 10ms/step - loss: 0.0226
Epoch:  58
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11/11 [==============================] - 0s 9ms/step - loss: 0.0224

11/11 [==============================] - 0s 10ms/step - loss: 0.0224
Epoch:  59
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11/11 [==============================] - 0s 9ms/step - loss: 0.0222

11/11 [==============================] - 0s 10ms/step - loss: 0.0222
Epoch:  60
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11/11 [==============================] - 0s 10ms/step - loss: 0.0220

11/11 [==============================] - 0s 11ms/step - loss: 0.0220
Epoch:  61
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11/11 [==============================] - 0s 9ms/step - loss: 0.0218

11/11 [==============================] - 0s 10ms/step - loss: 0.0218
Epoch:  62
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11/11 [==============================] - 0s 9ms/step - loss: 0.0217

11/11 [==============================] - 0s 10ms/step - loss: 0.0217
Epoch:  63
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11/11 [==============================] - 0s 9ms/step - loss: 0.0215

11/11 [==============================] - 0s 10ms/step - loss: 0.0215
Epoch:  64
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11/11 [==============================] - 0s 9ms/step - loss: 0.0213

11/11 [==============================] - 0s 10ms/step - loss: 0.0213
Epoch:  65
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11/11 [==============================] - 0s 9ms/step - loss: 0.0211

11/11 [==============================] - 0s 9ms/step - loss: 0.0211
Epoch:  66
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11/11 [==============================] - 0s 9ms/step - loss: 0.0209

11/11 [==============================] - 0s 10ms/step - loss: 0.0209
Epoch:  67
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11/11 [==============================] - 0s 9ms/step - loss: 0.0207

11/11 [==============================] - 0s 10ms/step - loss: 0.0207
Epoch:  68
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11/11 [==============================] - 0s 9ms/step - loss: 0.0205

11/11 [==============================] - 0s 9ms/step - loss: 0.0205
Epoch:  69
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11/11 [==============================] - 0s 9ms/step - loss: 0.0203

11/11 [==============================] - 0s 10ms/step - loss: 0.0203
Epoch:  70
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11/11 [==============================] - 0s 9ms/step - loss: 0.0201

11/11 [==============================] - 0s 10ms/step - loss: 0.0201
Epoch:  71
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11/11 [==============================] - 0s 9ms/step - loss: 0.0200

11/11 [==============================] - 0s 10ms/step - loss: 0.0200
Epoch:  72
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11/11 [==============================] - 0s 9ms/step - loss: 0.0198

11/11 [==============================] - 0s 9ms/step - loss: 0.0198
Epoch:  73
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11/11 [==============================] - 0s 9ms/step - loss: 0.0196

11/11 [==============================] - 0s 10ms/step - loss: 0.0196
Epoch:  74
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11/11 [==============================] - 0s 9ms/step - loss: 0.0195

11/11 [==============================] - 0s 10ms/step - loss: 0.0195
Epoch:  75
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11/11 [==============================] - 0s 9ms/step - loss: 0.0193

11/11 [==============================] - 0s 9ms/step - loss: 0.0193
Epoch:  76
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11/11 [==============================] - 0s 9ms/step - loss: 0.0192

11/11 [==============================] - 0s 10ms/step - loss: 0.0192
Epoch:  77
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11/11 [==============================] - 0s 13ms/step - loss: 0.0190

11/11 [==============================] - 0s 14ms/step - loss: 0.0190
Epoch:  78
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11/11 [==============================] - 0s 10ms/step - loss: 0.0189

11/11 [==============================] - 0s 12ms/step - loss: 0.0189
Epoch:  79
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11/11 [==============================] - 0s 9ms/step - loss: 0.0188

11/11 [==============================] - 0s 9ms/step - loss: 0.0188
Epoch:  80
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11/11 [==============================] - 0s 9ms/step - loss: 0.0186

11/11 [==============================] - 0s 10ms/step - loss: 0.0186
Epoch:  81
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11/11 [==============================] - 0s 9ms/step - loss: 0.0185

11/11 [==============================] - 0s 10ms/step - loss: 0.0185
Epoch:  82
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11/11 [==============================] - 0s 9ms/step - loss: 0.0184

11/11 [==============================] - 0s 9ms/step - loss: 0.0184
Epoch:  83
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11/11 [==============================] - 0s 9ms/step - loss: 0.0183

11/11 [==============================] - 0s 10ms/step - loss: 0.0183
Epoch:  84
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11/11 [==============================] - 0s 9ms/step - loss: 0.0181

11/11 [==============================] - 0s 9ms/step - loss: 0.0181
Epoch:  85
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11/11 [==============================] - 0s 9ms/step - loss: 0.0180

11/11 [==============================] - 0s 10ms/step - loss: 0.0180
Epoch:  86
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11/11 [==============================] - 0s 9ms/step - loss: 0.0178

11/11 [==============================] - 0s 9ms/step - loss: 0.0178
Epoch:  87
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11/11 [==============================] - 0s 13ms/step - loss: 0.0177

11/11 [==============================] - 0s 14ms/step - loss: 0.0177
Epoch:  88
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11/11 [==============================] - 0s 9ms/step - loss: 0.0175

11/11 [==============================] - 0s 10ms/step - loss: 0.0175
Epoch:  89
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11/11 [==============================] - 0s 9ms/step - loss: 0.0175

11/11 [==============================] - 0s 9ms/step - loss: 0.0175
Epoch:  90
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11/11 [==============================] - 0s 9ms/step - loss: 0.0175

11/11 [==============================] - 0s 10ms/step - loss: 0.0175
Epoch:  91
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11/11 [==============================] - 0s 9ms/step - loss: 0.0190

11/11 [==============================] - 0s 10ms/step - loss: 0.0190
Epoch:  92
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10/11 [==========================>...] - ETA: 0s - loss: 0.0218
11/11 [==============================] - 0s 12ms/step - loss: 0.0261

11/11 [==============================] - 0s 14ms/step - loss: 0.0261
Epoch:  93
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11/11 [==============================] - 0s 9ms/step - loss: 0.0520

11/11 [==============================] - 0s 10ms/step - loss: 0.0520
Epoch:  94
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11/11 [==============================] - 0s 9ms/step - loss: 0.1088

11/11 [==============================] - 0s 9ms/step - loss: 0.1088
Epoch:  95
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11/11 [==============================] - 0s 9ms/step - loss: 0.0837

11/11 [==============================] - 0s 10ms/step - loss: 0.0837
Epoch:  96
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11/11 [==============================] - 0s 12ms/step - loss: 0.0785

11/11 [==============================] - 0s 13ms/step - loss: 0.0785
Epoch:  97
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11/11 [==============================] - 0s 9ms/step - loss: 0.0342

11/11 [==============================] - 0s 10ms/step - loss: 0.0342
Epoch:  98
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11/11 [==============================] - 0s 9ms/step - loss: 0.0206

11/11 [==============================] - 0s 9ms/step - loss: 0.0206
Epoch:  99
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11/11 [==============================] - 0s 12ms/step - loss: 0.0216

11/11 [==============================] - 0s 12ms/step - loss: 0.0216
Epoch:  100
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11/11 [==============================] - 0s 9ms/step - loss: 0.0198

11/11 [==============================] - 0s 9ms/step - loss: 0.0198
Epoch:  101
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11/11 [==============================] - 0s 9ms/step - loss: 0.0180

11/11 [==============================] - 0s 10ms/step - loss: 0.0180
Epoch:  102
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11/11 [==============================] - 0s 9ms/step - loss: 0.0174

11/11 [==============================] - 0s 10ms/step - loss: 0.0174
Epoch:  103
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10/11 [==========================>...] - ETA: 0s - loss: 0.0183
11/11 [==============================] - 0s 12ms/step - loss: 0.0173

11/11 [==============================] - 0s 14ms/step - loss: 0.0173
Epoch:  104
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11/11 [==============================] - 0s 11ms/step - loss: 0.0172

11/11 [==============================] - 0s 12ms/step - loss: 0.0172
Epoch:  105
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11/11 [==============================] - 0s 8ms/step - loss: 0.0170

11/11 [==============================] - 0s 9ms/step - loss: 0.0170
Epoch:  106
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11/11 [==============================] - 0s 9ms/step - loss: 0.0169

11/11 [==============================] - 0s 10ms/step - loss: 0.0169
Epoch:  107
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11/11 [==============================] - 0s 9ms/step - loss: 0.0168

11/11 [==============================] - 0s 9ms/step - loss: 0.0168
Epoch:  108
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11/11 [==============================] - 0s 9ms/step - loss: 0.0167

11/11 [==============================] - 0s 9ms/step - loss: 0.0167
Epoch:  109
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11/11 [==============================] - 0s 11ms/step - loss: 0.0166

11/11 [==============================] - 0s 13ms/step - loss: 0.0166
Epoch:  110
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11/11 [==============================] - 0s 8ms/step - loss: 0.0166

11/11 [==============================] - 0s 14ms/step - loss: 0.0166
Epoch:  111
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11/11 [==============================] - 0s 9ms/step - loss: 0.0165

11/11 [==============================] - 0s 10ms/step - loss: 0.0165
Epoch:  112
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11/11 [==============================] - 0s 9ms/step - loss: 0.0164

11/11 [==============================] - 0s 9ms/step - loss: 0.0164
Epoch:  113
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11/11 [==============================] - 0s 8ms/step - loss: 0.0163

11/11 [==============================] - 0s 9ms/step - loss: 0.0163
Epoch:  114
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11/11 [==============================] - 0s 8ms/step - loss: 0.0163

11/11 [==============================] - 0s 9ms/step - loss: 0.0163
Epoch:  115
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11/11 [==============================] - 0s 9ms/step - loss: 0.0162

11/11 [==============================] - 0s 9ms/step - loss: 0.0162
Epoch:  116
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11/11 [==============================] - 0s 9ms/step - loss: 0.0161

11/11 [==============================] - 0s 9ms/step - loss: 0.0161
Epoch:  117
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11/11 [==============================] - 0s 13ms/step - loss: 0.0161

11/11 [==============================] - 0s 14ms/step - loss: 0.0161
Epoch:  118
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11/11 [==============================] - 0s 9ms/step - loss: 0.0160

11/11 [==============================] - 0s 10ms/step - loss: 0.0160
Epoch:  119
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11/11 [==============================] - 0s 9ms/step - loss: 0.0159

11/11 [==============================] - 0s 9ms/step - loss: 0.0159
Epoch:  120
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11/11 [==============================] - 0s 9ms/step - loss: 0.0159

11/11 [==============================] - 0s 10ms/step - loss: 0.0159
Epoch:  121
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11/11 [==============================] - 0s 9ms/step - loss: 0.0158

11/11 [==============================] - 0s 10ms/step - loss: 0.0158
Epoch:  122
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11/11 [==============================] - 0s 9ms/step - loss: 0.0157

11/11 [==============================] - 0s 10ms/step - loss: 0.0157
Epoch:  123
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11/11 [==============================] - 0s 9ms/step - loss: 0.0157

11/11 [==============================] - 0s 9ms/step - loss: 0.0157
Epoch:  124
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11/11 [==============================] - 0s 9ms/step - loss: 0.0156

11/11 [==============================] - 0s 10ms/step - loss: 0.0156
Epoch:  125
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11/11 [==============================] - 0s 16ms/step - loss: 0.0156

11/11 [==============================] - 0s 17ms/step - loss: 0.0156
Epoch:  126
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11/11 [==============================] - 0s 9ms/step - loss: 0.0155

11/11 [==============================] - 0s 10ms/step - loss: 0.0155
Epoch:  127
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11/11 [==============================] - 0s 9ms/step - loss: 0.0155

11/11 [==============================] - 0s 10ms/step - loss: 0.0155
Epoch:  128
 1/11 [=>............................] - ETA: 0s - loss: 0.0101
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11/11 [==============================] - 0s 8ms/step - loss: 0.0155

11/11 [==============================] - 0s 9ms/step - loss: 0.0155
Epoch:  129
 1/11 [=>............................] - ETA: 0s - loss: 0.0100
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11/11 [==============================] - 0s 9ms/step - loss: 0.0154

11/11 [==============================] - 0s 10ms/step - loss: 0.0154
Epoch:  130
 1/11 [=>............................] - ETA: 0s - loss: 0.0100
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11/11 [==============================] - 0s 9ms/step - loss: 0.0154

11/11 [==============================] - 0s 10ms/step - loss: 0.0154
Epoch:  131
 1/11 [=>............................] - ETA: 0s - loss: 0.0098
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11/11 [==============================] - 0s 10ms/step - loss: 0.0154

11/11 [==============================] - 0s 11ms/step - loss: 0.0154
Epoch:  132
 1/11 [=>............................] - ETA: 0s - loss: 0.0098
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11/11 [==============================] - 0s 8ms/step - loss: 0.0154

11/11 [==============================] - 0s 9ms/step - loss: 0.0154
Epoch:  133
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11/11 [==============================] - 0s 9ms/step - loss: 0.0156

11/11 [==============================] - 0s 10ms/step - loss: 0.0156
Epoch:  134
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11/11 [==============================] - 0s 9ms/step - loss: 0.0159

11/11 [==============================] - 0s 10ms/step - loss: 0.0159
Epoch:  135
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11/11 [==============================] - 0s 9ms/step - loss: 0.0171

11/11 [==============================] - 0s 10ms/step - loss: 0.0171
Epoch:  136
 1/11 [=>............................] - ETA: 0s - loss: 0.0100
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11/11 [==============================] - 0s 9ms/step - loss: 0.0201

11/11 [==============================] - 0s 10ms/step - loss: 0.0201
Epoch:  137
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11/11 [==============================] - 0s 9ms/step - loss: 0.0274

11/11 [==============================] - 0s 10ms/step - loss: 0.0274
Epoch:  138
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11/11 [==============================] - 0s 9ms/step - loss: 0.0422

11/11 [==============================] - 0s 9ms/step - loss: 0.0422
Epoch:  139
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11/11 [==============================] - 0s 9ms/step - loss: 0.0446

11/11 [==============================] - 0s 10ms/step - loss: 0.0446
Epoch:  140
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11/11 [==============================] - 0s 9ms/step - loss: 0.0454

11/11 [==============================] - 0s 9ms/step - loss: 0.0454
Epoch:  141
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11/11 [==============================] - 0s 9ms/step - loss: 0.0233

11/11 [==============================] - 0s 9ms/step - loss: 0.0233
Epoch:  142
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11/11 [==============================] - 0s 8ms/step - loss: 0.0178

11/11 [==============================] - 0s 9ms/step - loss: 0.0178
Epoch:  143
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11/11 [==============================] - 0s 9ms/step - loss: 0.0164

11/11 [==============================] - 0s 10ms/step - loss: 0.0164
Epoch:  144
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11/11 [==============================] - 0s 9ms/step - loss: 0.0167

11/11 [==============================] - 0s 10ms/step - loss: 0.0167
Epoch:  145
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11/11 [==============================] - 0s 9ms/step - loss: 0.0163

11/11 [==============================] - 0s 10ms/step - loss: 0.0163
Epoch:  146
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11/11 [==============================] - 0s 9ms/step - loss: 0.0162

11/11 [==============================] - 0s 10ms/step - loss: 0.0162
Epoch:  147
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11/11 [==============================] - 0s 9ms/step - loss: 0.0157

11/11 [==============================] - 0s 10ms/step - loss: 0.0157
Epoch:  148
 1/11 [=>............................] - ETA: 0s - loss: 0.0090
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11/11 [==============================] - 0s 9ms/step - loss: 0.0157

11/11 [==============================] - 0s 10ms/step - loss: 0.0157
Epoch:  149
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11/11 [==============================] - 0s 8ms/step - loss: 0.0154

11/11 [==============================] - 0s 9ms/step - loss: 0.0154
Epoch:  150
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11/11 [==============================] - 0s 9ms/step - loss: 0.0155

11/11 [==============================] - 0s 10ms/step - loss: 0.0155
Epoch:  151
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11/11 [==============================] - 0s 9ms/step - loss: 0.0154

11/11 [==============================] - 0s 10ms/step - loss: 0.0154
Epoch:  152
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11/11 [==============================] - 0s 9ms/step - loss: 0.0155

11/11 [==============================] - 0s 10ms/step - loss: 0.0155
Epoch:  153
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11/11 [==============================] - 0s 9ms/step - loss: 0.0153

11/11 [==============================] - 0s 10ms/step - loss: 0.0153
Epoch:  154
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11/11 [==============================] - 0s 9ms/step - loss: 0.0155

11/11 [==============================] - 0s 9ms/step - loss: 0.0155
Epoch:  155
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11/11 [==============================] - 0s 9ms/step - loss: 0.0155

11/11 [==============================] - 0s 10ms/step - loss: 0.0155
Epoch:  156
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11/11 [==============================] - 0s 10ms/step - loss: 0.0158

11/11 [==============================] - 0s 11ms/step - loss: 0.0158
Epoch:  157
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11/11 [==============================] - 0s 9ms/step - loss: 0.0160

11/11 [==============================] - 0s 10ms/step - loss: 0.0160
Epoch:  158
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11/11 [==============================] - 0s 10ms/step - loss: 0.0166

11/11 [==============================] - 0s 11ms/step - loss: 0.0166
Epoch:  159
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11/11 [==============================] - 0s 8ms/step - loss: 0.0173

11/11 [==============================] - 0s 9ms/step - loss: 0.0173
Epoch:  160
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11/11 [==============================] - 0s 9ms/step - loss: 0.0183

11/11 [==============================] - 0s 10ms/step - loss: 0.0183
Epoch:  161
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11/11 [==============================] - 0s 9ms/step - loss: 0.0202

11/11 [==============================] - 0s 9ms/step - loss: 0.0202
Epoch:  162
 1/11 [=>............................] - ETA: 0s - loss: 0.0083
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11/11 [==============================] - 0s 9ms/step - loss: 0.0212

11/11 [==============================] - 0s 10ms/step - loss: 0.0212
Epoch:  163
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11/11 [==============================] - 0s 14ms/step - loss: 0.0241

11/11 [==============================] - 0s 15ms/step - loss: 0.0241
Epoch:  164
 1/11 [=>............................] - ETA: 0s - loss: 0.0083
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11/11 [==============================] - 0s 9ms/step - loss: 0.0227

11/11 [==============================] - 0s 10ms/step - loss: 0.0227
Epoch:  165
 1/11 [=>............................] - ETA: 0s - loss: 0.0089
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11/11 [==============================] - 0s 9ms/step - loss: 0.0240

11/11 [==============================] - 0s 10ms/step - loss: 0.0240
Epoch:  166
 1/11 [=>............................] - ETA: 0s - loss: 0.0083
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11/11 [==============================] - 0s 9ms/step - loss: 0.0208

11/11 [==============================] - 0s 10ms/step - loss: 0.0208
Epoch:  167
 1/11 [=>............................] - ETA: 0s - loss: 0.0084
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11/11 [==============================] - 0s 9ms/step - loss: 0.0198

11/11 [==============================] - 0s 9ms/step - loss: 0.0198
Epoch:  168
 1/11 [=>............................] - ETA: 0s - loss: 0.0082
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11/11 [==============================] - 0s 9ms/step - loss: 0.0182

11/11 [==============================] - 0s 10ms/step - loss: 0.0182
Epoch:  169
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11/11 [==============================] - 0s 13ms/step - loss: 0.0171

11/11 [==============================] - 0s 15ms/step - loss: 0.0171
Epoch:  170
 1/11 [=>............................] - ETA: 0s - loss: 0.0082
 7/11 [==================>...........] - ETA: 0s - loss: 0.0169
11/11 [==============================] - 0s 9ms/step - loss: 0.0167

11/11 [==============================] - 0s 10ms/step - loss: 0.0167
Epoch:  171
 1/11 [=>............................] - ETA: 0s - loss: 0.0081
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11/11 [==============================] - 0s 9ms/step - loss: 0.0161

11/11 [==============================] - 0s 10ms/step - loss: 0.0161
Epoch:  172
 1/11 [=>............................] - ETA: 0s - loss: 0.0080
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11/11 [==============================] - 0s 9ms/step - loss: 0.0160

11/11 [==============================] - 0s 9ms/step - loss: 0.0160
Epoch:  173
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11/11 [==============================] - 0s 9ms/step - loss: 0.0157

11/11 [==============================] - 0s 10ms/step - loss: 0.0157
Epoch:  174
 1/11 [=>............................] - ETA: 0s - loss: 0.0080
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11/11 [==============================] - 0s 9ms/step - loss: 0.0159

11/11 [==============================] - 0s 10ms/step - loss: 0.0159
Epoch:  175
 1/11 [=>............................] - ETA: 0s - loss: 0.0081
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11/11 [==============================] - 0s 9ms/step - loss: 0.0157

11/11 [==============================] - 0s 10ms/step - loss: 0.0157
Epoch:  176
 1/11 [=>............................] - ETA: 0s - loss: 0.0079
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10/11 [==========================>...] - ETA: 0s - loss: 0.0169
11/11 [==============================] - 0s 16ms/step - loss: 0.0159

11/11 [==============================] - 0s 17ms/step - loss: 0.0159
Epoch:  177
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11/11 [==============================] - 0s 9ms/step - loss: 0.0159

11/11 [==============================] - 0s 9ms/step - loss: 0.0159
Epoch:  178
 1/11 [=>............................] - ETA: 0s - loss: 0.0078
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11/11 [==============================] - 0s 9ms/step - loss: 0.0161

11/11 [==============================] - 0s 10ms/step - loss: 0.0161
Epoch:  179
 1/11 [=>............................] - ETA: 0s - loss: 0.0081
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11/11 [==============================] - 0s 9ms/step - loss: 0.0163

11/11 [==============================] - 0s 9ms/step - loss: 0.0163
Epoch:  180
 1/11 [=>............................] - ETA: 0s - loss: 0.0077
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11/11 [==============================] - 0s 10ms/step - loss: 0.0166

11/11 [==============================] - 0s 10ms/step - loss: 0.0166
Epoch:  181
 1/11 [=>............................] - ETA: 0s - loss: 0.0081
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11/11 [==============================] - 0s 9ms/step - loss: 0.0171

11/11 [==============================] - 0s 9ms/step - loss: 0.0171
Epoch:  182
 1/11 [=>............................] - ETA: 0s - loss: 0.0077
 7/11 [==================>...........] - ETA: 0s - loss: 0.0184
11/11 [==============================] - 0s 9ms/step - loss: 0.0175

11/11 [==============================] - 0s 10ms/step - loss: 0.0175
Epoch:  183
 1/11 [=>............................] - ETA: 0s - loss: 0.0081
 7/11 [==================>...........] - ETA: 0s - loss: 0.0197
11/11 [==============================] - 0s 9ms/step - loss: 0.0184

11/11 [==============================] - 0s 10ms/step - loss: 0.0184
Epoch:  184
 1/11 [=>............................] - ETA: 0s - loss: 0.0076
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11/11 [==============================] - 0s 9ms/step - loss: 0.0185

11/11 [==============================] - 0s 10ms/step - loss: 0.0185
Epoch:  185
 1/11 [=>............................] - ETA: 0s - loss: 0.0081
 7/11 [==================>...........] - ETA: 0s - loss: 0.0214
11/11 [==============================] - 0s 9ms/step - loss: 0.0196

11/11 [==============================] - 0s 10ms/step - loss: 0.0196
Epoch:  186
 1/11 [=>............................] - ETA: 0s - loss: 0.0076
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11/11 [==============================] - 0s 9ms/step - loss: 0.0190

11/11 [==============================] - 0s 10ms/step - loss: 0.0190
Epoch:  187
 1/11 [=>............................] - ETA: 0s - loss: 0.0080
 7/11 [==================>...........] - ETA: 0s - loss: 0.0219
11/11 [==============================] - 0s 9ms/step - loss: 0.0199

11/11 [==============================] - 0s 10ms/step - loss: 0.0199
Epoch:  188
 1/11 [=>............................] - ETA: 0s - loss: 0.0076
 7/11 [==================>...........] - ETA: 0s - loss: 0.0204
11/11 [==============================] - 0s 9ms/step - loss: 0.0188

11/11 [==============================] - 0s 10ms/step - loss: 0.0188
Epoch:  189
 1/11 [=>............................] - ETA: 0s - loss: 0.0079
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11/11 [==============================] - 0s 9ms/step - loss: 0.0190

11/11 [==============================] - 0s 10ms/step - loss: 0.0190
Epoch:  190
 1/11 [=>............................] - ETA: 0s - loss: 0.0076
 7/11 [==================>...........] - ETA: 0s - loss: 0.0191
11/11 [==============================] - 0s 9ms/step - loss: 0.0180

11/11 [==============================] - 0s 15ms/step - loss: 0.0180
Epoch:  191
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11/11 [==============================] - 0s 9ms/step - loss: 0.0178

11/11 [==============================] - 0s 10ms/step - loss: 0.0178
Epoch:  192
 1/11 [=>............................] - ETA: 0s - loss: 0.0075
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11/11 [==============================] - 0s 9ms/step - loss: 0.0172

11/11 [==============================] - 0s 9ms/step - loss: 0.0172
Epoch:  193
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11/11 [==============================] - 0s 9ms/step - loss: 0.0168

11/11 [==============================] - 0s 10ms/step - loss: 0.0168
Epoch:  194
 1/11 [=>............................] - ETA: 0s - loss: 0.0075
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11/11 [==============================] - 0s 9ms/step - loss: 0.0166

11/11 [==============================] - 0s 9ms/step - loss: 0.0166
Epoch:  195
 1/11 [=>............................] - ETA: 0s - loss: 0.0075
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11/11 [==============================] - 0s 9ms/step - loss: 0.0162

11/11 [==============================] - 0s 10ms/step - loss: 0.0162
Epoch:  196
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11/11 [==============================] - 0s 9ms/step - loss: 0.0162

11/11 [==============================] - 0s 9ms/step - loss: 0.0162
Epoch:  197
 1/11 [=>............................] - ETA: 0s - loss: 0.0075
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11/11 [==============================] - 0s 9ms/step - loss: 0.0160

11/11 [==============================] - 0s 9ms/step - loss: 0.0160
Epoch:  198
 1/11 [=>............................] - ETA: 0s - loss: 0.0073
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11/11 [==============================] - 0s 9ms/step - loss: 0.0160

11/11 [==============================] - 0s 9ms/step - loss: 0.0160
Epoch:  199
 1/11 [=>............................] - ETA: 0s - loss: 0.0075
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11/11 [==============================] - 0s 8ms/step - loss: 0.0160

11/11 [==============================] - 0s 9ms/step - loss: 0.0160
Epoch:  200
 1/11 [=>............................] - ETA: 0s - loss: 0.0073
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11/11 [==============================] - 0s 9ms/step - loss: 0.0160

11/11 [==============================] - 0s 9ms/step - loss: 0.0160
Epoch:  201
 1/11 [=>............................] - ETA: 0s - loss: 0.0075
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11/11 [==============================] - 0s 8ms/step - loss: 0.0162

11/11 [==============================] - 0s 9ms/step - loss: 0.0162
Epoch:  202
 1/11 [=>............................] - ETA: 0s - loss: 0.0072
 7/11 [==================>...........] - ETA: 0s - loss: 0.0165
11/11 [==============================] - 0s 9ms/step - loss: 0.0163

11/11 [==============================] - 0s 9ms/step - loss: 0.0163
Epoch:  203
 1/11 [=>............................] - ETA: 0s - loss: 0.0075
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11/11 [==============================] - 0s 16ms/step - loss: 0.0166

11/11 [==============================] - 0s 17ms/step - loss: 0.0166
Epoch:  204
 1/11 [=>............................] - ETA: 0s - loss: 0.0072
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11/11 [==============================] - 0s 8ms/step - loss: 0.0168

11/11 [==============================] - 0s 9ms/step - loss: 0.0168
Epoch:  205
 1/11 [=>............................] - ETA: 0s - loss: 0.0075
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11/11 [==============================] - 0s 9ms/step - loss: 0.0174

11/11 [==============================] - 0s 9ms/step - loss: 0.0174
Epoch:  206
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11/11 [==============================] - 0s 9ms/step - loss: 0.0175

11/11 [==============================] - 0s 9ms/step - loss: 0.0175
Epoch:  207
 1/11 [=>............................] - ETA: 0s - loss: 0.0075
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11/11 [==============================] - 0s 9ms/step - loss: 0.0182

11/11 [==============================] - 0s 10ms/step - loss: 0.0182
Epoch:  208
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11/11 [==============================] - 0s 8ms/step - loss: 0.0181

11/11 [==============================] - 0s 9ms/step - loss: 0.0181
Epoch:  209
 1/11 [=>............................] - ETA: 0s - loss: 0.0075
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11/11 [==============================] - 0s 9ms/step - loss: 0.0187

11/11 [==============================] - 0s 10ms/step - loss: 0.0187
Epoch:  210
 1/11 [=>............................] - ETA: 0s - loss: 0.0071
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11/11 [==============================] - 0s 8ms/step - loss: 0.0182

11/11 [==============================] - 0s 9ms/step - loss: 0.0182
Epoch:  211
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11/11 [==============================] - 0s 10ms/step - loss: 0.0185

11/11 [==============================] - 0s 11ms/step - loss: 0.0185
Epoch:  212
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11/11 [==============================] - 0s 9ms/step - loss: 0.0178

11/11 [==============================] - 0s 9ms/step - loss: 0.0178
Epoch:  213
 1/11 [=>............................] - ETA: 0s - loss: 0.0073
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11/11 [==============================] - 0s 9ms/step - loss: 0.0178

11/11 [==============================] - 0s 9ms/step - loss: 0.0178
Epoch:  214
 1/11 [=>............................] - ETA: 0s - loss: 0.0070
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11/11 [==============================] - 0s 9ms/step - loss: 0.0172

11/11 [==============================] - 0s 9ms/step - loss: 0.0172
Epoch:  215
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11/11 [==============================] - 0s 9ms/step - loss: 0.0170

11/11 [==============================] - 0s 9ms/step - loss: 0.0170
Epoch:  216
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11/11 [==============================] - 0s 9ms/step - loss: 0.0166

11/11 [==============================] - 0s 10ms/step - loss: 0.0166
Epoch:  217
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10/11 [==========================>...] - ETA: 0s - loss: 0.0175
11/11 [==============================] - 0s 14ms/step - loss: 0.0163

11/11 [==============================] - 0s 15ms/step - loss: 0.0163
Epoch:  218
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11/11 [==============================] - 0s 10ms/step - loss: 0.0162

11/11 [==============================] - 0s 11ms/step - loss: 0.0162
Epoch:  219
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11/11 [==============================] - 0s 9ms/step - loss: 0.0159

11/11 [==============================] - 0s 10ms/step - loss: 0.0159
Epoch:  220
 1/11 [=>............................] - ETA: 0s - loss: 0.0069
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11/11 [==============================] - 0s 9ms/step - loss: 0.0158

11/11 [==============================] - 0s 10ms/step - loss: 0.0158
Epoch:  221
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11/11 [==============================] - 0s 9ms/step - loss: 0.0156

11/11 [==============================] - 0s 9ms/step - loss: 0.0156
Epoch:  222
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11/11 [==============================] - 0s 9ms/step - loss: 0.0156

11/11 [==============================] - 0s 9ms/step - loss: 0.0156
Epoch:  223
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11/11 [==============================] - 0s 9ms/step - loss: 0.0155

11/11 [==============================] - 0s 10ms/step - loss: 0.0155
Epoch:  224
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11/11 [==============================] - 0s 9ms/step - loss: 0.0156

11/11 [==============================] - 0s 9ms/step - loss: 0.0156
Epoch:  225
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11/11 [==============================] - 0s 9ms/step - loss: 0.0157

11/11 [==============================] - 0s 10ms/step - loss: 0.0157
Epoch:  226
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11/11 [==============================] - 0s 9ms/step - loss: 0.0158

11/11 [==============================] - 0s 9ms/step - loss: 0.0158
Epoch:  227
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11/11 [==============================] - 0s 8ms/step - loss: 0.0162

11/11 [==============================] - 0s 9ms/step - loss: 0.0162
Epoch:  228
 1/11 [=>............................] - ETA: 0s - loss: 0.0067
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11/11 [==============================] - 0s 9ms/step - loss: 0.0165

11/11 [==============================] - 0s 9ms/step - loss: 0.0165
Epoch:  229
 1/11 [=>............................] - ETA: 0s - loss: 0.0070
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11/11 [==============================] - 0s 9ms/step - loss: 0.0172

11/11 [==============================] - 0s 9ms/step - loss: 0.0172
Epoch:  230
 1/11 [=>............................] - ETA: 0s - loss: 0.0066
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11/11 [==============================] - 0s 9ms/step - loss: 0.0176

11/11 [==============================] - 0s 10ms/step - loss: 0.0176
Epoch:  231
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11/11 [==============================] - 0s 9ms/step - loss: 0.0188

11/11 [==============================] - 0s 10ms/step - loss: 0.0188
Epoch:  232
 1/11 [=>............................] - ETA: 0s - loss: 0.0066
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11/11 [==============================] - 0s 9ms/step - loss: 0.0189

11/11 [==============================] - 0s 10ms/step - loss: 0.0189
Epoch:  233
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11/11 [==============================] - 0s 9ms/step - loss: 0.0199

11/11 [==============================] - 0s 9ms/step - loss: 0.0199
Epoch:  234
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11/11 [==============================] - 0s 9ms/step - loss: 0.0190

11/11 [==============================] - 0s 10ms/step - loss: 0.0190
Epoch:  235
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11/11 [==============================] - 0s 9ms/step - loss: 0.0192

11/11 [==============================] - 0s 9ms/step - loss: 0.0192
Epoch:  236
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11/11 [==============================] - 0s 9ms/step - loss: 0.0180

11/11 [==============================] - 0s 9ms/step - loss: 0.0180
Epoch:  237
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11/11 [==============================] - 0s 9ms/step - loss: 0.0181

11/11 [==============================] - 0s 10ms/step - loss: 0.0181
Epoch:  238
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11/11 [==============================] - 0s 9ms/step - loss: 0.0175

11/11 [==============================] - 0s 19ms/step - loss: 0.0175
Epoch:  239
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11/11 [==============================] - 0s 10ms/step - loss: 0.0172

11/11 [==============================] - 0s 10ms/step - loss: 0.0172
Epoch:  240
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11/11 [==============================] - 0s 15ms/step - loss: 0.0170

11/11 [==============================] - 0s 16ms/step - loss: 0.0170
Epoch:  241
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11/11 [==============================] - 0s 9ms/step - loss: 0.0162

11/11 [==============================] - 0s 10ms/step - loss: 0.0162
Epoch:  242
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11/11 [==============================] - 0s 9ms/step - loss: 0.0160

11/11 [==============================] - 0s 10ms/step - loss: 0.0160
Epoch:  243
 1/11 [=>............................] - ETA: 0s - loss: 0.0066
 7/11 [==================>...........] - ETA: 0s - loss: 0.0149
11/11 [==============================] - 0s 9ms/step - loss: 0.0154

11/11 [==============================] - 0s 9ms/step - loss: 0.0154
Epoch:  244
 1/11 [=>............................] - ETA: 0s - loss: 0.0065
 7/11 [==================>...........] - ETA: 0s - loss: 0.0145
11/11 [==============================] - 0s 9ms/step - loss: 0.0153

11/11 [==============================] - 0s 9ms/step - loss: 0.0153
Epoch:  245
 1/11 [=>............................] - ETA: 0s - loss: 0.0066
 7/11 [==================>...........] - ETA: 0s - loss: 0.0146
11/11 [==============================] - 0s 9ms/step - loss: 0.0150

11/11 [==============================] - 0s 9ms/step - loss: 0.0150
Epoch:  246
 1/11 [=>............................] - ETA: 0s - loss: 0.0064
 7/11 [==================>...........] - ETA: 0s - loss: 0.0143
11/11 [==============================] - 0s 9ms/step - loss: 0.0150

11/11 [==============================] - 0s 10ms/step - loss: 0.0150
Epoch:  247
 1/11 [=>............................] - ETA: 0s - loss: 0.0066
 7/11 [==================>...........] - ETA: 0s - loss: 0.0145
11/11 [==============================] - 0s 9ms/step - loss: 0.0149

11/11 [==============================] - 0s 9ms/step - loss: 0.0149
Epoch:  248
 1/11 [=>............................] - ETA: 0s - loss: 0.0064
 7/11 [==================>...........] - ETA: 0s - loss: 0.0143
11/11 [==============================] - 0s 9ms/step - loss: 0.0150

11/11 [==============================] - 0s 10ms/step - loss: 0.0150
Epoch:  249
 1/11 [=>............................] - ETA: 0s - loss: 0.0065
 7/11 [==================>...........] - ETA: 0s - loss: 0.0147
11/11 [==============================] - 0s 9ms/step - loss: 0.0150

11/11 [==============================] - 0s 10ms/step - loss: 0.0150
Epoch:  250
 1/11 [=>............................] - ETA: 0s - loss: 0.0063
 7/11 [==================>...........] - ETA: 0s - loss: 0.0146
11/11 [==============================] - 0s 9ms/step - loss: 0.0152

11/11 [==============================] - 0s 10ms/step - loss: 0.0152
Epoch:  251
 1/11 [=>............................] - ETA: 0s - loss: 0.0065
 7/11 [==================>...........] - ETA: 0s - loss: 0.0152
11/11 [==============================] - 0s 8ms/step - loss: 0.0154

11/11 [==============================] - 0s 9ms/step - loss: 0.0154
Epoch:  252
 1/11 [=>............................] - ETA: 0s - loss: 0.0063
 7/11 [==================>...........] - ETA: 0s - loss: 0.0152
11/11 [==============================] - 0s 9ms/step - loss: 0.0157

11/11 [==============================] - 0s 10ms/step - loss: 0.0157
Epoch:  253
 1/11 [=>............................] - ETA: 0s - loss: 0.0066
 7/11 [==================>...........] - ETA: 0s - loss: 0.0162
11/11 [==============================] - 0s 9ms/step - loss: 0.0162

11/11 [==============================] - 0s 10ms/step - loss: 0.0162
Epoch:  254
 1/11 [=>............................] - ETA: 0s - loss: 0.0063
 7/11 [==================>...........] - ETA: 0s - loss: 0.0161
11/11 [==============================] - 0s 9ms/step - loss: 0.0165

11/11 [==============================] - 0s 10ms/step - loss: 0.0165
Epoch:  255
 1/11 [=>............................] - ETA: 0s - loss: 0.0066
 7/11 [==================>...........] - ETA: 0s - loss: 0.0176
11/11 [==============================] - 0s 9ms/step - loss: 0.0174

11/11 [==============================] - 0s 9ms/step - loss: 0.0174
Epoch:  256
 1/11 [=>............................] - ETA: 0s - loss: 0.0063
 7/11 [==================>...........] - ETA: 0s - loss: 0.0174
11/11 [==============================] - 0s 8ms/step - loss: 0.0174

11/11 [==============================] - 0s 9ms/step - loss: 0.0174
Epoch:  257
 1/11 [=>............................] - ETA: 0s - loss: 0.0067
 7/11 [==================>...........] - ETA: 0s - loss: 0.0192
11/11 [==============================] - 0s 9ms/step - loss: 0.0186

11/11 [==============================] - 0s 9ms/step - loss: 0.0186
Epoch:  258
 1/11 [=>............................] - ETA: 0s - loss: 0.0062
 7/11 [==================>...........] - ETA: 0s - loss: 0.0185
11/11 [==============================] - 0s 9ms/step - loss: 0.0181

11/11 [==============================] - 0s 10ms/step - loss: 0.0181
Epoch:  259
 1/11 [=>............................] - ETA: 0s - loss: 0.0067
 7/11 [==================>...........] - ETA: 0s - loss: 0.0202
11/11 [==============================] - 0s 8ms/step - loss: 0.0191

11/11 [==============================] - 0s 9ms/step - loss: 0.0191
Epoch:  260
 1/11 [=>............................] - ETA: 0s - loss: 0.0063
 7/11 [==================>...........] - ETA: 0s - loss: 0.0187
11/11 [==============================] - 0s 10ms/step - loss: 0.0180

11/11 [==============================] - 0s 10ms/step - loss: 0.0180
Epoch:  261
 1/11 [=>............................] - ETA: 0s - loss: 0.0066
 7/11 [==================>...........] - ETA: 0s - loss: 0.0198
11/11 [==============================] - 0s 10ms/step - loss: 0.0186

11/11 [==============================] - 0s 11ms/step - loss: 0.0186
Epoch:  262
 1/11 [=>............................] - ETA: 0s - loss: 0.0063
 7/11 [==================>...........] - ETA: 0s - loss: 0.0179
11/11 [==============================] - 0s 9ms/step - loss: 0.0175

11/11 [==============================] - 0s 10ms/step - loss: 0.0175
Epoch:  263
 1/11 [=>............................] - ETA: 0s - loss: 0.0064
 7/11 [==================>...........] - ETA: 0s - loss: 0.0182
11/11 [==============================] - 0s 9ms/step - loss: 0.0176

11/11 [==============================] - 0s 10ms/step - loss: 0.0176
Epoch:  264
 1/11 [=>............................] - ETA: 0s - loss: 0.0063
 7/11 [==================>...........] - ETA: 0s - loss: 0.0166
11/11 [==============================] - 0s 9ms/step - loss: 0.0171

11/11 [==============================] - 0s 10ms/step - loss: 0.0171
Epoch:  265
 1/11 [=>............................] - ETA: 0s - loss: 0.0063
 7/11 [==================>...........] - ETA: 0s - loss: 0.0166
11/11 [==============================] - 0s 10ms/step - loss: 0.0168

11/11 [==============================] - 0s 10ms/step - loss: 0.0168
Epoch:  266
 1/11 [=>............................] - ETA: 0s - loss: 0.0062
 7/11 [==================>...........] - ETA: 0s - loss: 0.0155
11/11 [==============================] - 0s 9ms/step - loss: 0.0166

11/11 [==============================] - 0s 10ms/step - loss: 0.0166
Epoch:  267
 1/11 [=>............................] - ETA: 0s - loss: 0.0063
 7/11 [==================>...........] - ETA: 0s - loss: 0.0154
11/11 [==============================] - 0s 10ms/step - loss: 0.0161

11/11 [==============================] - 0s 11ms/step - loss: 0.0161
Epoch:  268
 1/11 [=>............................] - ETA: 0s - loss: 0.0062
 7/11 [==================>...........] - ETA: 0s - loss: 0.0147
10/11 [==========================>...] - ETA: 0s - loss: 0.0164
11/11 [==============================] - 0s 16ms/step - loss: 0.0160

11/11 [==============================] - 0s 18ms/step - loss: 0.0160
Epoch:  269
 1/11 [=>............................] - ETA: 0s - loss: 0.0062
 6/11 [===============>..............] - ETA: 0s - loss: 0.0129
11/11 [==============================] - 0s 10ms/step - loss: 0.0154

11/11 [==============================] - 0s 11ms/step - loss: 0.0154
Epoch:  270
 1/11 [=>............................] - ETA: 0s - loss: 0.0061
 7/11 [==================>...........] - ETA: 0s - loss: 0.0142
11/11 [==============================] - 0s 9ms/step - loss: 0.0153

11/11 [==============================] - 0s 10ms/step - loss: 0.0153
Epoch:  271
 1/11 [=>............................] - ETA: 0s - loss: 0.0062
 7/11 [==================>...........] - ETA: 0s - loss: 0.0143
11/11 [==============================] - 0s 9ms/step - loss: 0.0149

11/11 [==============================] - 0s 10ms/step - loss: 0.0149
Epoch:  272
 1/11 [=>............................] - ETA: 0s - loss: 0.0060
 7/11 [==================>...........] - ETA: 0s - loss: 0.0140
11/11 [==============================] - 0s 9ms/step - loss: 0.0149

11/11 [==============================] - 0s 9ms/step - loss: 0.0149
Epoch:  273
 1/11 [=>............................] - ETA: 0s - loss: 0.0061
 7/11 [==================>...........] - ETA: 0s - loss: 0.0143
11/11 [==============================] - 0s 9ms/step - loss: 0.0148

11/11 [==============================] - 0s 9ms/step - loss: 0.0148
Epoch:  274
 1/11 [=>............................] - ETA: 0s - loss: 0.0060
 7/11 [==================>...........] - ETA: 0s - loss: 0.0141
11/11 [==============================] - 0s 9ms/step - loss: 0.0148

11/11 [==============================] - 0s 10ms/step - loss: 0.0148
Epoch:  275
 1/11 [=>............................] - ETA: 0s - loss: 0.0061
 7/11 [==================>...........] - ETA: 0s - loss: 0.0145
11/11 [==============================] - 0s 9ms/step - loss: 0.0149

11/11 [==============================] - 0s 9ms/step - loss: 0.0149
Epoch:  276
 1/11 [=>............................] - ETA: 0s - loss: 0.0059
 7/11 [==================>...........] - ETA: 0s - loss: 0.0143
11/11 [==============================] - 0s 9ms/step - loss: 0.0151

11/11 [==============================] - 0s 10ms/step - loss: 0.0151
Epoch:  277
 1/11 [=>............................] - ETA: 0s - loss: 0.0061
 7/11 [==================>...........] - ETA: 0s - loss: 0.0150
11/11 [==============================] - 0s 9ms/step - loss: 0.0153

11/11 [==============================] - 0s 9ms/step - loss: 0.0153
Epoch:  278
 1/11 [=>............................] - ETA: 0s - loss: 0.0059
 7/11 [==================>...........] - ETA: 0s - loss: 0.0149
 9/11 [=======================>......] - ETA: 0s - loss: 0.0183
11/11 [==============================] - 0s 14ms/step - loss: 0.0155

11/11 [==============================] - 0s 16ms/step - loss: 0.0155
Epoch:  279
 1/11 [=>............................] - ETA: 0s - loss: 0.0061
 7/11 [==================>...........] - ETA: 0s - loss: 0.0158
11/11 [==============================] - 0s 9ms/step - loss: 0.0161

11/11 [==============================] - 0s 9ms/step - loss: 0.0161
Epoch:  280
 1/11 [=>............................] - ETA: 0s - loss: 0.0059
 7/11 [==================>...........] - ETA: 0s - loss: 0.0158
11/11 [==============================] - 0s 9ms/step - loss: 0.0163

11/11 [==============================] - 0s 10ms/step - loss: 0.0163
Epoch:  281
 1/11 [=>............................] - ETA: 0s - loss: 0.0062
 7/11 [==================>...........] - ETA: 0s - loss: 0.0171
11/11 [==============================] - 0s 9ms/step - loss: 0.0172

11/11 [==============================] - 0s 10ms/step - loss: 0.0172
Epoch:  282
 1/11 [=>............................] - ETA: 0s - loss: 0.0059
 7/11 [==================>...........] - ETA: 0s - loss: 0.0169
11/11 [==============================] - 0s 9ms/step - loss: 0.0173

11/11 [==============================] - 0s 10ms/step - loss: 0.0173
Epoch:  283
 1/11 [=>............................] - ETA: 0s - loss: 0.0062
 7/11 [==================>...........] - ETA: 0s - loss: 0.0186
11/11 [==============================] - 0s 9ms/step - loss: 0.0183

11/11 [==============================] - 0s 10ms/step - loss: 0.0183
Epoch:  284
 1/11 [=>............................] - ETA: 0s - loss: 0.0059
 7/11 [==================>...........] - ETA: 0s - loss: 0.0179
11/11 [==============================] - 0s 9ms/step - loss: 0.0178

11/11 [==============================] - 0s 9ms/step - loss: 0.0178
Epoch:  285
 1/11 [=>............................] - ETA: 0s - loss: 0.0062
 7/11 [==================>...........] - ETA: 0s - loss: 0.0195
10/11 [==========================>...] - ETA: 0s - loss: 0.0199
11/11 [==============================] - 0s 15ms/step - loss: 0.0187

11/11 [==============================] - 0s 17ms/step - loss: 0.0187
Epoch:  286
 1/11 [=>............................] - ETA: 0s - loss: 0.0059
 7/11 [==================>...........] - ETA: 0s - loss: 0.0182
11/11 [==============================] - 0s 9ms/step - loss: 0.0177

11/11 [==============================] - 0s 9ms/step - loss: 0.0177
Epoch:  287
 1/11 [=>............................] - ETA: 0s - loss: 0.0062
 7/11 [==================>...........] - ETA: 0s - loss: 0.0192
11/11 [==============================] - 0s 9ms/step - loss: 0.0183

11/11 [==============================] - 0s 10ms/step - loss: 0.0183
Epoch:  288
 1/11 [=>............................] - ETA: 0s - loss: 0.0059
 7/11 [==================>...........] - ETA: 0s - loss: 0.0175
11/11 [==============================] - 0s 9ms/step - loss: 0.0174

11/11 [==============================] - 0s 9ms/step - loss: 0.0174
Epoch:  289
 1/11 [=>............................] - ETA: 0s - loss: 0.0061
 7/11 [==================>...........] - ETA: 0s - loss: 0.0179
11/11 [==============================] - 0s 9ms/step - loss: 0.0176

11/11 [==============================] - 0s 10ms/step - loss: 0.0176
Epoch:  290
 1/11 [=>............................] - ETA: 0s - loss: 0.0059
 7/11 [==================>...........] - ETA: 0s - loss: 0.0164
11/11 [==============================] - 0s 9ms/step - loss: 0.0173

11/11 [==============================] - 0s 10ms/step - loss: 0.0173
Epoch:  291
 1/11 [=>............................] - ETA: 0s - loss: 0.0060
 7/11 [==================>...........] - ETA: 0s - loss: 0.0164
11/11 [==============================] - 0s 8ms/step - loss: 0.0171

11/11 [==============================] - 0s 9ms/step - loss: 0.0171
Epoch:  292
 1/11 [=>............................] - ETA: 0s - loss: 0.0059
 7/11 [==================>...........] - ETA: 0s - loss: 0.0153
10/11 [==========================>...] - ETA: 0s - loss: 0.0170
11/11 [==============================] - 0s 14ms/step - loss: 0.0171

11/11 [==============================] - 0s 16ms/step - loss: 0.0171
Epoch:  293
 1/11 [=>............................] - ETA: 0s - loss: 0.0059
 7/11 [==================>...........] - ETA: 0s - loss: 0.0152
11/11 [==============================] - 0s 8ms/step - loss: 0.0163

11/11 [==============================] - 0s 9ms/step - loss: 0.0163
Epoch:  294
 1/11 [=>............................] - ETA: 0s - loss: 0.0058
 7/11 [==================>...........] - ETA: 0s - loss: 0.0145
11/11 [==============================] - 0s 8ms/step - loss: 0.0162

11/11 [==============================] - 0s 9ms/step - loss: 0.0162
Epoch:  295
 1/11 [=>............................] - ETA: 0s - loss: 0.0059
 7/11 [==================>...........] - ETA: 0s - loss: 0.0145
11/11 [==============================] - 0s 9ms/step - loss: 0.0155

11/11 [==============================] - 0s 9ms/step - loss: 0.0155
Epoch:  296
 1/11 [=>............................] - ETA: 0s - loss: 0.0058
 7/11 [==================>...........] - ETA: 0s - loss: 0.0141
11/11 [==============================] - 0s 9ms/step - loss: 0.0153

11/11 [==============================] - 0s 10ms/step - loss: 0.0153
Epoch:  297
 1/11 [=>............................] - ETA: 0s - loss: 0.0058
 7/11 [==================>...........] - ETA: 0s - loss: 0.0142
11/11 [==============================] - 0s 9ms/step - loss: 0.0149

11/11 [==============================] - 0s 10ms/step - loss: 0.0149
Epoch:  298
 1/11 [=>............................] - ETA: 0s - loss: 0.0057
 7/11 [==================>...........] - ETA: 0s - loss: 0.0139
11/11 [==============================] - 0s 9ms/step - loss: 0.0149

11/11 [==============================] - 0s 10ms/step - loss: 0.0149
Epoch:  299
 1/11 [=>............................] - ETA: 0s - loss: 0.0058
 7/11 [==================>...........] - ETA: 0s - loss: 0.0142
11/11 [==============================] - 0s 9ms/step - loss: 0.0148

11/11 [==============================] - 0s 9ms/step - loss: 0.0148
Epoch:  300

1+3

[1] 4

Data aktual, prediksi, dan gabungan

actual <- future_value[1:3200,]
prediksi <- future_value[3201:3320,]

prediksi$value <- prediksi$value/2

hasil <- rbind(actual, prediksi)

Membuat tabel data prediksi

prediksi %>%
  kbl() %>%
  kable_material(c("striped", "hover"))

index	value	key
2022-04-28	3385.745	predict
2022-04-29	3380.317	predict
2022-04-30	3388.455	predict
2022-05-01	3380.317	predict
2022-05-02	3363.969	predict
2022-05-03	3442.067	predict
2022-05-04	3426.104	predict
2022-05-05	3401.964	predict
2022-05-06	3418.084	predict
2022-05-07	3399.268	predict
2022-05-08	3393.867	predict
2022-05-09	3442.067	predict
2022-05-10	3489.293	predict
2022-05-11	3499.648	predict
2022-05-12	3489.293	predict
2022-05-13	3484.097	predict
2022-05-14	3455.287	predict
2022-05-15	3434.099	predict
2022-05-16	3393.867	predict
2022-05-17	3347.529	predict
2022-05-18	3342.029	predict
2022-05-19	3322.705	predict
2022-05-20	3317.162	predict
2022-05-21	3336.520	predict
2022-05-22	3336.520	predict
2022-05-23	3363.969	predict
2022-05-24	3380.317	predict
2022-05-25	3366.700	predict
2022-05-26	3342.029	predict
2022-05-27	3336.520	predict
2022-05-28	3319.935	predict
2022-05-29	3303.268	predict
2022-05-30	3300.483	predict
2022-05-31	3283.726	predict
2022-06-01	3278.124	predict
2022-06-02	3283.726	predict
2022-06-03	3317.162	predict
2022-06-04	3319.935	predict
2022-06-05	3311.611	predict
2022-06-06	3294.905	predict
2022-06-07	3614.454	predict
2022-06-08	3643.413	predict
2022-06-09	3650.850	predict
2022-06-10	3638.345	predict
2022-06-11	3638.310	predict
2022-06-12	3657.180	predict
2022-06-13	3660.798	predict
2022-06-14	3637.095	predict
2022-06-15	3678.403	predict
2022-06-16	3658.866	predict
2022-06-17	3644.868	predict
2022-06-18	3639.978	predict
2022-06-19	3640.213	predict
2022-06-20	3639.700	predict
2022-06-21	3637.793	predict
2022-06-22	3648.888	predict
2022-06-23	3625.729	predict
2022-06-24	3615.519	predict
2022-06-25	3622.064	predict
2022-06-26	3588.371	predict
2022-06-27	3583.658	predict
2022-06-28	3570.708	predict
2022-06-29	3573.839	predict
2022-06-30	3570.832	predict
2022-07-01	3573.519	predict
2022-07-02	3578.419	predict
2022-07-03	3584.074	predict
2022-07-04	3568.755	predict
2022-07-05	3564.904	predict
2022-07-06	3540.996	predict
2022-07-07	3520.221	predict
2022-07-08	3518.365	predict
2022-07-09	3517.091	predict
2022-07-10	3497.888	predict
2022-07-11	3500.815	predict
2022-07-12	3503.553	predict
2022-07-13	3507.843	predict
2022-07-14	3511.865	predict
2022-07-15	3471.386	predict
2022-07-16	3460.490	predict
2022-07-17	3870.268	predict
2022-07-18	3874.143	predict
2022-07-19	3838.235	predict
2022-07-20	3822.342	predict
2022-07-21	3818.848	predict
2022-07-22	3769.330	predict
2022-07-23	3789.527	predict
2022-07-24	3791.296	predict
2022-07-25	3786.755	predict
2022-07-26	3775.956	predict
2022-07-27	3817.395	predict
2022-07-28	3771.557	predict
2022-07-29	3719.164	predict
2022-07-30	3734.075	predict
2022-07-31	3749.266	predict
2022-08-01	3736.346	predict
2022-08-02	3749.287	predict
2022-08-03	3769.128	predict
2022-08-04	3815.895	predict
2022-08-05	3848.838	predict
2022-08-06	3905.867	predict
2022-08-07	3903.805	predict
2022-08-08	3899.621	predict
2022-08-09	3873.534	predict
2022-08-10	3898.913	predict
2022-08-11	3861.919	predict
2022-08-12	3844.083	predict
2022-08-13	3843.828	predict
2022-08-14	3842.372	predict
2022-08-15	3853.927	predict
2022-08-16	3840.274	predict
2022-08-17	3841.922	predict
2022-08-18	3841.035	predict
2022-08-19	3836.106	predict
2022-08-20	3870.601	predict
2022-08-21	3850.620	predict
2022-08-22	3835.568	predict
2022-08-23	3896.567	predict
2022-08-24	3934.167	predict
2022-08-25	3923.645	predict

NA

Plot khusus data hasil prediksi

prediksi %>% 
  ggplot(aes(x = index, y = value)) +
  geom_line(color = "olivedrab2", size = 1.2) + 
  geom_point(size = 0.5, color = "mintcream") +
  theme_solarized_2(light = FALSE) +
  labs(
    title = 'UNVR Forecasted Price',
    subtitle = '120 future point - Apr to Aug 2022') +
  xlab("Date") + ylab("Forecasted Close Price")

Gabungan data prediksi dengan data awal

hasil_fix <- hasil_merge %>%
  rename(Actual = value) %>%
  left_join(
    tibble(
      index = actual$index,
      Actual = pull(actual, value))) %>%
  left_join(
    tibble(
      index = prediksi$index,
      Prediksi = pull(prediksi, value))) 

Joining, by = c("index", "Actual")
Joining, by = "index"

Plot seluruhnya

options(warn = -4)
hasil_fix %>% 
  gather(
    key = key, value = value,
    Actual, Prediksi
  ) %>%
  mutate(
    key = key %>% factor(levels = c("Actual", "Prediksi"))
  ) %>%
  ggplot(aes(x = index, y = value, colour = key)) +
  geom_line(size = 1) + 
  scale_color_manual(values = c(
    "Actual" = "mintcream",
    "Prediksi" = "olivedrab2"
  )) + theme_solarized_2(light = FALSE) + 
  labs(
    title = 'UNVR Price',
    subtitle = 'Actual (from 2021) and Future Prediction') +
  xlab("Date") + ylab("Price")

Untuk visualisasi lebih jelas, misalkan kita plot data mulai 2020

options(warn = -4)
hasil_fix[2875:3320,] %>% 
  gather(
    key = key, value = value,
    Actual, Prediksi
  ) %>%
  mutate(
    key = key %>% factor(levels = c("Actual", "Prediksi"))
  ) %>%
  ggplot(aes(x = index, y = value, colour = key)) +
  geom_line(size = 1) + 
  scale_color_manual(values = c(
    "Actual" = "mintcream",
    "Prediksi" = "olivedrab2"
  )) + theme_solarized_2(light = FALSE) + 
  labs(
    title = 'UNVR Price',
    subtitle = 'Actual and Future Prediction') +
  xlab("Date") + ylab("Close Price")

END